利用深度学习的HEVC帧内编码单元快速划分算法

新一代视频编码标准高效视频编码(High Efficiency Video Coding,HEVC)中编码单元(Coding Unit,CU)大小不同的特性使得编码效率得到显著提升,但同时带来了极高的计算复杂度.为了去除CU划分中多余的计算从而降低编码复杂度,本文提出了一种利用深度学习的编码单元快速划分算法.首先使用原始视频亮度块及编码信息建立了一个HEVC中CU划分的数据库,用于接下来本文深度学习神经网络的训练.然后,为了更好地贴合编码单元划分的层级结构,本文提出了一种基于Inception模块的神经网络结构,使之内嵌于HEVC编码框架中对编码单元的划分进行提前预测,有效地去除了All Intra配置下中冗余的CU划分计算.实验结果表明,本文提出的算法与HEVC官方测试模型(HM16.12)相比,编码时间平均降低了61.31%,而BD-BR与BD-PSNR仅为1.86%和-0.13dB.

Fast HEVC Coding Units Partitioning Algorithm Based on Deep Learning

The different size of the coding unit(CU)in the new generation of videocoding standard,highefficiency video coding(HEVC)makes the coding efficiency significantly improved,but at the same time brings high computational complexity.In order to remove the redundant calculation in CU partition and reduce the coding complexity,this paper proposes a fast partitioning algorithm based on deep learning.Firstly,a database of CU partition in HEVC is established by the original video luminance block and coding information,which provides data guarantee for trainingthedeep learning model.Then,in order to better fit the hierarchical structure of coding unit partition,this paper uses a neural network structure based on Inception Module,which isembeddedin the HEVC coding framework to predict the partition of coding units in advance,effectively eliminating the redundant CU partition calculation in All Intra configuration.The experimental results show that compared with the HEVC official test model(HM16.12),the proposed algorithm reduces the coding time by an average of 61.31%,while the BD-BR and BD-PSNR are only 1.86%and-0.13dB.